Lund Plane to Bloch (LP2B) encoding for object and polarization tagging with quantum jet substructure

Abstract The application of quantum algorithms to jet substructure analysis is of growing interest as Noisy Intermediate-Scale Quantum (NISQ) hardware continues to mature in qubit count and gate depth. Jet substructure remains essential for addressing challenges at the LHC and beyond, notably object classification and polarization tagging. However, existing quantum machine learning approaches typically rely on data representations that suffer from infrared and collinear (IRC) unsafety, sensitivity to non-perturbative effects, or poor scalability. In this work, we introduce the Lund Plane to Bloch (LP2B) encoding, which maps a theoretically clean and robust representation of jet kinematics directly into qubit states. Leveraging this encoding, we implement a Quantum Tree-Topology Network (QTTN) that natively embeds the hierarchical structure of the Lund tree. We evaluate the QTTN across multiple benchmarks, comparing it with classical machine learning architectures and the standard “one particle – one qubit” (1P1Q) encoding on polarization, W boson, and top quark tagging tasks, including in the low-data regime. The results show that, despite its low parameter count, the QTTN achieves competitive performance with classical baselines, demonstrates enhanced sensitivity compared to the 1P1Q encoding on polarization and W tagging, improves the performance-to-cost trade-off, and exhibits a gentle degradation in the low-data regime and reduced sensitivity to generator-specific parton shower and hadronization models. Finally, the QTTN is validated on real quantum hardware using a 3-qubit liquid-state NMR SpinQ device.